With a flood of tools on the market and hype outpacing understanding, it’s hard to know where to begin.
Should you build or buy? What will actually move the needle for your legal team? And what should you have in place before rolling anything out?
In this guide, we cut through the noise. Whether you're a seasoned GC or just starting your legal ops journey, you’ll find practical advice on evaluating Legal AI, making smart decisions, and avoiding costly missteps.
Introduction to legal AI
What started as cautious experiments with clause extraction or keyword tagging has evolved into a wave of transformative legal AI technologies, from generative AI drafting entire contracts to agentic tools adding redlines to legal documents autonomously.
What once sat on innovation roadmaps is now in production, powering workflows across in-house teams, law firms, and legal tech providers.
The early 2010s saw a flurry of interest in AI for law, mainly rule-based engines and machine learning tools that could parse documents or score risk. But adoption was slow. Tools were clunky. Legal work was too nuanced, too contextual — and frankly, most AI wasn’t good enough to trust.
Then came 2022. The release of large language models (LLMs) like GPT-3.5 and GPT-4 changed the equation. For the first time, lawyers could interact with AI in natural language — and the AI could respond with outputs that felt not just usable, but genuinely useful.
That leads us nicely into where we are today, with more than 90 per cent of in-house lawyers using these generative AI tools either daily or weekly, and almost all of the lawyers we recently surveyed saying they believe that AI will change their job over the course of a year.
Results from our recent survey of in-house lawyers.
Key benefits of AI in law
There’s been no shortage of noise about AI replacing lawyers. It’s a conversation that’s sparked anxiety in the legal profession for years, and understandably so.
But at Juro, we don’t believe it’ll go that way.
AI isn’t replacing lawyers — it’s making them faster, sharper, and more effective at what they do best. For in-house teams, that means fewer manual tasks, shorter contract cycles, and more time to focus on the strategic work that actually moves the needle for the business.
Legal AI is at its best when it’s helping lawyers get rid of the repetitive, time-consuming work — the boilerplate, the clause comparisons, the endless formatting — and letting them set the rules that AI then follows. It’s not about giving up control. It’s about applying your judgement where it counts, and letting legal automation do the rest.
Essentially, this benefits lawyers in three key ways.
1. More strategic work, less repetitive admin
By taking care of the high-volume, low-value tasks — like reviewing standard terms, checking for clause consistency, or approving routine contracts like NDAs — AI frees up legal teams to focus on the work that really needs their input: advising on risk, negotiating complex deals, and shaping business strategy.
By doing this, legal AI shifts legal’s role from reactive support to proactive partner: advising on business risk, navigating complex deals, and contributing to strategic decisions without being buried in admin.
2. Better decisions, powered by data
AI tools can analyse large volumes of contract data quickly — pulling trends, identifying anomalies, and surfacing hidden risks across your contract portfolio.
For example, you can instantly see how termination clauses vary across hundreds of MSAs, or which contracts are missing key obligations. That kind of insight used to take days, but now it takes seconds.
Legal AI tools like Juro leverage AI to extract key datapoints from your contracts, making it ready to report on.
3. Real cost savings
AI reduces the need for external legal spend on routine matters — like contract review, redlining, and first-pass due diligence because it automates it.
It also cuts internal time spent on manual legal work, helping smaller teams support growing businesses without scaling headcount at the same pace. The net result: a more efficient legal function that delivers more value, without the extra cost.
In an environment where AI is redefining value in the legal industry, AI-driven solutions and alternative providers are proving that high-quality legal work doesn’t have to come with excessive fees or inefficiencies” - Richard Mabey, CEO at Juro
Legal AI use cases
Legal AI has matured from a niche experiment into a core capability. Today, it’s not just helping lawyers work faster — it’s transforming how legal teams operate, how they collaborate with the business, and how they deliver value at scale.
Below are the key ways in-house legal teams are already putting AI to work — not just in contracts, but across the full legal function — along with the tools helping them do it.
1. Contract drafting and review at scale
Legal AI tools like Juro’s can draft entire contracts in seconds, drawing from automated contract templates, playbooks, and prior agreements to ensure alignment with company standards. But it doesn’t stop at drafting.
Juro’s agentic AI also reviews contracts for deviations from pre-approved positions, suggests redlines, and can even explain the rationale behind its edits — saving lawyers hours per document.
These capabilities are especially valuable in high-volume areas like NDAs, MSAs, or SLAs, where speed matters but risk can’t be compromised. Instead of manually combing through each clause, lawyers can trust AI to handle the heavy lifting, surfacing only the outliers that truly need attention.
Popular legal AI tools for this:
Juro – Drafts, reviews, and redlines contracts end-to-end using generative and agentic AI.
Harvey – LLM-powered legal assistant for sophisticated contract review and regulatory analysis.
Spellbook – Contract review assistant embedded into Microsoft Word.
Juro's contract review agent is now available in Slack.
Juro’s AI-powered contract review agent now allows third-party contracts to be reviewed and redlined against your contract playbooks. This is available in Juro, Slack, Microsoft Teams, and Microsoft Word, making this fully automated contract review functionality accessible where your teams already work.
If that sounds like something you'd benefit from, hit the button below to see it in action.
2. Playbook enforcement and clause standardisation
AI tools can monitor whether incoming contracts conform to your standard contract playbook — and flag deviations in real time. For example, if a supplier sends over terms with an unusually aggressive indemnity clause, the AI can detect it instantly, suggest fallback language, and escalate to legal if thresholds are exceeded.
This kind of automated consistency reduces the likelihood of risk slipping through unnoticed, and ensures contracts are aligned with evolving company policies.
Popular legal AI tools for this:
Juro – Flags clause deviations and automates redlines based on pre-approved playbooks.
Robin AI – Automatically applies fallback logic and clause swaps at scale.
Lexis+ AI – Matches language in incoming contracts to internal policy documents.
3. Legal intake and ticketing
Many in-house teams are also using AI to streamline legal intake. Instead of managing ad-hoc emails or Slack messages, AI tools can categorise, route, and even respond to incoming legal requests.
For example, if someone asks, “Can I use this contractor agreement for a freelancer in Germany?”, an AI tool might pull up the relevant template, check jurisdiction-specific requirements, and respond with a recommended next step — or escalate to legal for input if needed.
Popular legal AI tools for this:
Juro – Contract intake forms and AI-powered routing within contract workflows.
LawVu – Legal ticketing with automation and self-service intake logic.
TangoEye – NLP-driven request categorisation and response handling.
4. Litigation support and due diligence
In more advanced use cases, legal AI is also assisting with litigation preparation or M&A diligence. By reviewing large volumes of contracts and surfacing relevant clauses, obligations, and risks, AI dramatically reduces the time needed to prepare disclosures or identify red flags.
Some teams use AI to flag renewal terms, change-of-control clauses, or exclusivity provisions — insights that would previously take days to compile manually.
Popular legal AI tools for this:
RelativityOne – Industry standard for AI-driven eDiscovery and document review.
DISCO AI – Advanced predictive coding for litigation preparation. Kira Systems – Used in both litigation prep and compliance audits.
5. Policy, regulatory and compliance monitoring
AI tools are now able to analyse regulations and map them to internal policies — ensuring compliance and flagging gaps.
From GDPR audits to identifying where new employment laws affect existing templates, AI can highlight risks without a team spending weeks in spreadsheets.
Popular legal AI tools for this:
Harvey – Can be used to summarise and contextualise evolving regulations.
Imprima Smart Compliance – Monitors legal obligations across jurisdictions and ties them to internal docs.
Relativity AI – Applied in compliance investigations and monitoring large datasets for regulatory issues.
6. Knowledge management and precedent search
In-house teams often have hundreds of legacy contracts, memos, or policies. However, surfacing relevant ones quickly can be near impossible.
With AI-powered search and summarisation, teams can find the “closest match” precedent or playbook in seconds, transforming how legal knowledge is accessed and reused.
Popular legal AI tools for this:
Kira Systems – Extracts and classifies data from thousands of legacy contracts.
Lexion – Indexes contract repositories and enables fast clause- or metadata-level search.
Evisort – Surfacing precedent and clause variations across enterprise contract databases.
7. Strategic advice and business partnering
AI enables lawyers to focus on complex strategic work — advising on risk, supporting go-to-market teams, or unblocking deals — because it handles the repetitive low-value tasks.
But more than that, AI can help simulate legal outcomes, summarise risks, and even suggest negotiation strategies based on past deals.
Popular legal AI tools for this:
Juro – Automates contracting so legal can spend time advising on what matters.
CoCounsel (Casetext) – Used for rapid legal research, case summaries, and structured legal argument development.
Luminance – Applies legal reasoning to guide decision-making in M&A and audits.
8. Reporting, analytics, and insights
AI tools can extract contract metadata, identify clause trends, and benchmark across time or counterparties. That means teams can spot issues before they escalate — and make informed decisions faster.
For example, a GC can pull data for big questions like: “Which active contracts expose us to unlimited liability?” — and get an answer within seconds.
Popular legal AI tools for this:
Juro – Surfacing real-time contract insights and clause-level data across the business.
LinkSquares – Provides customisable contract dashboards and risk reports across contract portfolios.
Evisort – Advanced AI analytics and reporting features for enterprise teams.
Types of legal AI tools
The legal AI market is booming, but it’s also fragmented, fast-moving, and often hard to decode.
What’s the difference between an AI assistant and a contract lifecycle platform? Between copilots and agents? Between a “legal AI tool” and just good software that happens to use AI?
In reality, not all legal AI tools are created equal. And when you’re choosing a platform to bet on understanding what category a tool sits in — and what problem it’s best at solving — is just as important as what tech it uses under the hood.
Broadly speaking, legal AI tools can fit into a few categories:
Type of legal AI tool
Summary
Benefits
Drawbacks
Point solutions(e.g Spellbook, LawGeex, RobinAI, Harvey, PaxtonAI)
Standalone tools designed to solve a single legal task or use case — usually very well. Think redlining, legal research, due diligence, or clause comparison.
Fast to deploy and experiment with. Often best-in-class for the task at hand. Ideal for filling gaps in existing workflows. Usually lower cost and commitment than full platforms.
Risk of tool sprawl and disconnected data. May require legal ops to “glue” tools together. Can’t scale easily into adjacent workflows. Vary in explainability and configurability
AI embedded into CLMs(e.g Juro, Ironclad, Linksqaures, Evisort)
Contract lifecycle and legal operations platforms that embed AI throughout — from drafting and negotiation to approvals and analytics. These tools are built for end-to-end workflows, not just single tasks.
Centralises work into one platform. Reduces handoffs and manual steps. AI is context-aware (based on templates, history, users). Provides strong governance, access control, and audit trails.
Higher implementation effort upfront. May be overkill for smaller teams or narrow use cases. Some platforms bolt on AI later, rather than build natively. Limited flexibility if you want to heavily customise logic.
General purpose AI tools(e.g ChatGPT, Microsoft Copilot, Claude, Gemini)
Broad LLM-based tools or productivity platforms not built for legal, but used by lawyers via prompts or workflows. They’re flexible, accessible, and sometimes surprisingly powerful.
Very low barrier to entry and cost. Useful for experimentation, prototyping, or one-off tasks. Can be integrated into internal tools (e.g. Notion, Slack, Docs). Often good for summarisation, brainstorming, or clause drafting.
Outputs can be unreliable or lack legal nuance. Usually lack explainability or source-tracking. Risk of data exposure if not enterprise-grade. No built-in legal workflows or compliance logic.
Agents and Copilots(e.g Juro’s AI agent, Flank AI)
Copilots assist you by suggesting next steps. Agents go further — they complete tasks autonomously based on predefined logic and context.
Automate entire workflows, not just responses. Reduce manual involvement to near-zero. Can learn from prior interactions to personalise actions. Ideal for high-volume, high-consistency work.
Require more configuration and trust. Less suitable for tasks needing nuanced judgment. Still an emerging category — fewer mature examples.
Categories of legal AI software
There are also slightly more nuanced categories of legal AI software, with many focusing on delivering value at one particular stage of the contract lifecycle, for example.
1. Contract extraction software
Contract extraction software scans and parses contracts to pull out important information (like names, deadlines, or terms) and turns it into structured data. It's like turning messy PDFs into neat table views automatically, which makes it great for migrating old contracts or getting quick visibility into what’s in your agreements.
What it does
Extracts key metadata and clauses—like renewal dates, payment terms, governing law, and parties—from contracts into a structured format (e.g. spreadsheetsorcontract management platforms).
Reduces manual data entry, speeds up contract onboarding, and improves data accuracy.
Example use case
A legal ops team uses extraction software to import 1,000 legacy NDAs into a CLM system, tagging each with party names, dates, and key clauses for easy filtering.
Legal document generation software lets teams generate contracts by answering a few questions or syncing with a CRM—so instead of writing contracts manually, you're filling in blanks or letting software do it for you. It’s most helpful when creating lots of the same type of document.
What it does
Allows users to create custom contracts from templates by filling out questionnaires or integrating with CRM systems like HubSpot,SalesforceandPipedrive.
Where it's used
At the contract creation and negotiation stage, especially for high-volume agreements like NDAs orsales contracts.
Value delivered
Saves time, reduces errors, ensures consistency, and increases self-serve capabilitiesfor business teams.
Example use case
Sales reps use an automated NDA template connected to Salesforce to instantly generate compliant contracts without legal team involvement.
Contract review software behaves like a junior lawyer trained on your playbook—it scans contracts you're receiving (like from a vendor or partner), highlights risks, and even suggests changes. It speeds up review cycles and helps legal teams focus on the trickiest issues.
What it does
Analyzes incoming contracts and redlines them autonomously, identifying risky clauses, missing terms, and non-compliant language based on company playbooks. Also known ascontract redlining software.
Where it's used
During the contract negotiation phase, especially forthird-party paper.
Value delivered
Accelerates review cycles, supports faster deal closing, and ensures compliance with internal policies.
Example use case
A junior legal associate uses AI review software to quickly scan a vendor agreement, which flags an unfavorable indemnity clause for further review or proposes a new, more acceptable clause that aligns with your playbooks.
Rather than looking at contracts one at a time, contract intelligence software pulls insights from across a whole contract portfolio. It's like business analytics for your agreements, allowing you to spot the trends, risks, or revenue opportunities buried in thousands of pages of legal jargon.
What it does
Turnscontract datainto business intelligence by aggregating insights across large contract portfolios.
Where it's used
Post-signature, for risk monitoring, compliance tracking, and operational reporting.
Value delivered
Enables data-driven decisions by surfacing trends like renewal risks, common negotiation bottlenecks, orcontract value leakage.
Example use case
The finance team generates reports onauto-renewalsdue in the next quarter, pulled from thousands of supplier contracts.
Contract abstraction tools summarize contracts so you don’t have to read every word. They’re perfect for busy times like M&A, where you need to understand 500 agreements quickly. You get short-form versions showing the key obligations or clauses.
What it does
Summarizes key contractual terms and obligations into concise bullet points or summary documents. These are often referred to ascontract summaries, or abstracts.
Where it's used
Useful during M&A due diligence, audits, or handovers.
Value delivered
Speeds up understanding of large contract sets without needing full legal review of each.
Example use case
During an acquisition, a legal team uses abstraction tools to summarize 500 customer contracts, highlighting assignment clauses and termination rights.
An AI contracts generator is where generative AI meets legal drafting. You type a natural-language prompt like “create an NDA for a freelance designer,” and the software drafts it for you. It is one of the most basic AI solutions out there and is typically adopted by non-lawyers as a starting point.
What it does
Creates new contracts from scratch using natural language inputs, often driven by generative AI (e.g. "Generate an employment agreement for a London-based software engineer").
Contract analysis software doesn’t just extract data—it analyzes how your contracts stack up across a portfolio. It benchmarks terms, flags inconsistencies, and spots unusual patterns. Useful for audits, diligence, or just checking if you’re staying within policy.
What it does
Uses machine learning to understand, compare, and benchmark contracts or clauses across large datasets.
Where it's used
Both pre- and post-signature—for diligence, compliance audits, or contract portfolio analysis.
Value delivered
Reveals patterns and outliers in contractual terms that could pose risks or opportunities.
Example use case
A GC uses AI to analyze variation in liability caps across hundreds of supplier agreements, flagging those that diverge from standard terms and highlighting contractual risk.
This type of software scans and parses contracts to pull out important information (like names, deadlines, or terms) and turns it into structured data. It's like turning messy PDFs into organized Excel rows automatically—great for migrating old contracts or getting quick visibility into what’s in your agreements.
Legal AI chatbots vary in their scope. However, employees can typically ask them questions about contracts, clauses, or policies and receive instant, contextual answers based on your playbooks or past agreements. It helps legal scale support without drowning in repetitive questions.
What it does
Provides instant responses to legal queries via chat, often integrated into tools like Slack, Teams, or internal portals.
Where it's used
Throughout the contract lifecycle—helping users draft clauses, understand obligations, or request legal help.
Value delivered
Deflects routine legal questions, accelerates access to legal knowledge, and improves internal service levels.
Example use case
A chatbot trained on the company’s contract playbook helps sales reps understand fallback positions when a customer pushes back on a clause. contractual risk.
Unlike many tools that fit neatly into these boxes, Juro doesn’t just plug into one stage of the contract lifecycle — it combines functionality from multiple legal AI software categories into a single, browser-based platform, helping fast-scaling businesses manage contracts more efficiently from end to end.
Legal document automation
Juro empowers business teams to generate compliant contracts in seconds through structured templates and natural language AI prompts. Whether using smartfields, dropdowns, or AI-assisted clause generation, users create contracts without needing legal to get involved in every draft.
AI contract review
Juro’s AI Assistant reviews contracts in-browser, providing instant redlines, risk flags, and fallback suggestions aligned with your contract playbook. Best of all, this agentic AI means this all happens autonomously. This reduces legal review cycles and helps business teams close deals faster, even on third-party paper.
Contract abstraction and extraction
Juro’s AI identifies and pulls out key terms, clauses, and metadata from both legacy and in-flight contracts. This eliminates the need for manual tagging and makes contract data searchable, actionable, and audit-ready from day one, even for contracts on the other party’s paper.
Contract intelligence and analysis
With structured contract data captured at creation and enriched via AI, Juro delivers real-time insights into contract volumes, renewal dates, negotiation trends, and clause-level deviations—supporting smarter, data-led decisions across legal and commercial teams.
Emerging trends in legal AI
1. Agentic AI: the transition from assistive to autonomous
We’re in an era where AI doesn’t just help with tasks — it completes them.
Agentic AI refers to intelligent systems that can independently complete entire workflows, like reviewing a contract, redlining risky clauses, routing it to stakeholders, and even triggering approval flows.
Unlike simple prompt-based assistants, agentic tools make decisions based on context, user intent, and prior interactions — transforming them into digital teammates, not just tools.
Jake Jones, Co-founder of Flank, an AI agent, shared his great definition of agentic AI on our podcast recently:
Agents aren’t to be confused with copilots, though. There are some important distinctions between the two. To add some colour to this comparison, agents can perform tasks autonomously, whereas copilots can’t.
Let’s take a common AI use-case in legal: reviewing a contract. A copilot might read your contract and recommend stuff for you to do with it. Agree with this clause interpretation, action this reminder date, flag this deviation from your standard playbook, and so on.
An agent might read your contract, know all the context in the same way a copilot does - and instead of recommending actions, it takes them. It just does it.
That’s what we’re delivering to in-house legal and business teams. Juro’s AI contract review functionality enables teams to review and redline contracts with AI agents, meaning anyone can get approved redlines in a matter of minutes, without your GC on hand.
2. The build vs buy debate
Post-ChatGPT, it suddenly felt like every team could (and maybe should?) build their own legal AI tool. Open-source models were accessible, APIs were everywhere, and prompts became a new language.
It’s not quite the same as when YouTube made everyone a creator, but it’s close. So if software development has been democratized, what does it mean for one of the oldest tensions in legal technology: do you buy, or do you build?
If your pain point is summarizing a doc, you don’t need legal tech: you need ChatGPT. If you want to build a ticketing system, you don’t need legal tech: you need Zapier.
So far, so good. But … when you reach a certain level of complexity, or risk, or monetary value … your vibe-coded solutions will start to creak. And then break. Reviewing contracts in ChatGPT is a prime example.
Ultimately, you need to recognize where legal tech vendors can and should be adding value that can’t easily be reproduced with off-the-shelf tools.
You are not buying software. You are buying the experience and expertise of a vendor that deeply understands user behaviour and has iterated thousands of times to create the perfect solution. The question is, when you look at your vendors … do they deliver that?” - Richard Mabey, CEO at Juro
3. Explainable AI
In legal, trust isn’t a nice-to-have — it’s non-negotiable.
AI that delivers outputs without explanation — the so-called "black box" — might fly in consumer tools, but it simply doesn’t pass muster in a legal context. If your AI recommends deleting an indemnity clause, the very first question will be: why?
In practice, this means legal AI tools must now do more than provide answers. They need to:
Cite their sources: Point to the clause, playbook, or policy that supports their recommendation.
Show their logic: Explain how the decision was reached — was it pattern recognition? Risk weighting? Fallback logic?
Offer alternatives: Present options, not edicts — so humans stay in control of the final call.
Adapt to user feedback: Learn from overrides and rejections, building a feedback loop into the workflow.
Explainability transforms AI from an output engine into a partner in legal reasoning, and it’s the perfect aid for commercial teams that need to relay the explanation for redlines shared with counterparties, for example, without the support of an in-house lawyer.
Are you asking the right questions? Are you engaging in deep, thoughtful analysis? Can you communicate effectively with the business? These are the emerging markers of legal value.” - Richard Mabey, CEO at Juro
Legal AI agents
It’s early days, but the direction of travel is clear. Legal AI is moving from task-based tools to intelligent, proactive systems that work alongside lawyers — not just as assistants, but as autonomous teammates. In other words, AI agents.
These agents don’t just respond to prompts; they can follow goals, apply logic, and handle multi-step workflows independently, managing parts of the legal process from start to finish without constant human input.
For example, Juro’s AI agent can handle entire workflows: reviewing third-party contracts against your positions, applying redlines, notifying legal if something falls outside the rules, and escalating only when needed.
Results from our recent survey of in-house lawyers.
Legal’s role in regulating AI
As legal AI tools become more capable, it’s essential to stay grounded in the risks as well as the rewards.
For in-house legal teams, that means thinking carefully about how AI is deployed and where the boundaries should be.
Who’s responsible if the AI makes a mistake? How do you validate outputs? What data is being used to generate results? Should that data be inputted at all, and if so, how is it protected?
These aren’t hypothetical concerns. They are questions legal teams need to answer today.
But how can they do that when regulators themselves appear to be falling behind? When we surveyed in-house lawyers earlier this year, the vast majority believed that regulators had either little or no understanding of the technology they’re regulating. In fact, 66 per cent felt that way.
Results from our recent survey of in-house lawyers.
However, that doesn’t excuse lawyers from their duty to use AI responsibly and enforce that same expectation across their business. After all, we’re already seeing high-profile cases whereby companies leveraging AI in contentious ways have faced the letter of the law:
And that’s not all. New data has revealed that entry-level roles have dropped by a third since the launch of ChatGPT, meaning the advent of widely available AI tools has already had a real and measurable impact on the career prospects of the younger generation. This is true even in larger, well-resourced organizations like the UK’s Big Four, which are slashing graduate roles.
Add to that the fact that AI runs on energy, not magic, and it’s easy to visualize the impact AI has, and will continue to have, on environmental resources too:
Most large-scale AI deployments are housed in data centres, including those operated by cloud service providers. These data centres can take a heavy toll on the planet.” - The United Nations Environment Programme
Why are we labouring these points in a guide about legal AI? Well, it’s easy to think of legal AI as just another tooling decision: which solution should we buy to speed up contract reviews?
But the role of in-house legal goes far deeper than procurement. As AI reshapes how businesses build, operate and sell, legal sits at the centre of that change — advising on risks, enabling innovation, and setting the rules for responsible use.
Key considerations for lawyers
Ultimately, any in-house lawyer should have these three questions front of mind:
1. How can we responsibly build and sell AI?
If your business is developing AI products, legal has a key role in ensuring they're safe, compliant, and transparent from the outset. That means advising on IP rights, user consent, model explainability, and how risks are disclosed. It's not just about checking the box — it’s about helping build trust into the product.
2. How do we regulate AI use internally?
AI is being used across the business — by marketing, HR, sales, and ops. Legal teams should take the lead on setting internal guardrails: what’s allowed, what’s not, and where human oversight is required.That might mean drafting acceptable use policies, creating risk classifications for AI tools, or setting review thresholds for automated decisions.
Generative AI is transformational for legal work. But it’s not infallible. As a skilled lawyer or contract professional, your job is to engineer prompts and monitor output in a way that produces high quality output that you are prepared to stand behind.” - Michael Haynes, General Counsel at Juro
3. Are we implementing AI fairly and equitably?
We know by now that AI systems can inject bias into a decision-making process, just like humans can. In fact, studies have revealed that bias within AI often amplifies our own bias.
If those systems impact hiring, pricing, or customer interactions, the legal risks are very real, meaning in-house teams should proactively help ensure that fairness is baked into implementation. That includes reviewing training data, questioning outputs, and making sure AI isn’t reinforcing inequalities, even unintentionally.
Responsible AI means designing and using AI-powered systems in ways that align with human values and prevent harm.” - Michael Haynes, General Counsel at Juro
Legal AI implementation and adoption: best practices
Successful adoption isn’t just about picking a vendor or switching on a feature. It requires thoughtful planning, stakeholder buy-in, and iterative learning. We speak to lawyers daily, so we know firsthand that a lot of legal tech vendors are still failing their customers when it comes to adoption.
We know that CLM has an adoption problem. At Juro we take great pride in having the highest adoption rate in our category, according to independent G2 reviews. But there are solutions who I won’t name and shame - ones you’d have heard of - whose adoption rate is half of Juro’s” - Richard Mabey, CEO at Juro
Here’s how forward-thinking legal teams are making AI stick:
1. Start with a clearly defined problem, not the tech
AI isn’t a magic wand and it shouldn’t be adopted for its own sake. We know that contradicts what most organizations are hearing today, but the most effective implementations begin with a focused use case:
“We’re spending too long reviewing third-party contracts, and it’s stopping us from focusing on MSAs.”
“Sales can’t self-serve on NDAs without legal input, and it’s leading to longer sales cycles.”
“We have zero visibility into renewal management across our supplier agreements, and it’s leading to unexpected costs following missed auto-renewals.”
Like any project, begin by defining a pain point with measurable outcomes in mind, then explore how AI can address it. This is crucial because it enables you to identify which solutions actually solve your problems best, rather than which ones have the most hype.
2. Invest in its success (but rethink what “investment” looks like)
You don’t need to spend six months building clunky workflows in a legacy CLM to succeed with legal AI. But you do need to invest time and attention into the right things:
Refining the prompts and inputs that deliver the best results
Curating a contract playbook with clear guidance, fallback clauses, and risk thresholds
Teaching the tool how your business thinks about terms like liability, renewal, or exclusivity
Building simple, structured workflows that legal and business users can actually follow
After all, your output will only be as strong as your input, regardless of which tool you use. There’s little use investing in an expensive AI redlining solution only for it to mark your contracts up against guardrails that don’t match your own. Take the time to customize your workflows. You’ll thank yourself later.
Without metrics, momentum fizzles. We strongly recommend determining what success with your legal AI platform looks like early, and reporting on it consistently.
Key adoption and impact metrics might include:
Contracts created/reviewed with AI
Time saved per review
Reduction in legal escalations
Increased self-serve contract volume
Accuracy of AI vs human review
Time-to-sign improvements
Use these results to tell a compelling story internally — one that secures future or continued investment in legal AI tools that you know work.
The future isn’t about replacing lawyers; it’s about amplifying their capabilities. Legal AI tools will increasingly act as collaborative partners—working alongside teams to handle routine tasks, surface insights, and free up time for strategic work. This matters in scaling businesses, where lean legal teams are under pressure to deliver more with less.
For instance, AI-native contract management platforms already enable self-serve contracts. But what if these systems also had AI agents that could flag deviations from playbooks, highlight unusual contract terms, or benchmark agreements against market standards in real-time? This level of insight could redefine how legal teams approach their work.
Innovators who find ways to get at least some of that work at 1% of the price will win big. In their daily work, yes, (who wants to do grunt work?) but also in their career advancement" - Richard Mabey, CEO, Juro
A new era for legal teams
The future of legal AI tools is exciting and filled with potential. AI agents will bring unprecedented levels of efficiency, insight, and collaboration to legal teams.
But it’s not just about the technology itself; it’s about how these tools will reshape the role of legal in scaling businesses—enabling teams to work smarter, faster, and with more impact.
This is something we're working closely with legal teams on in 2025 and beyond. To be one of them, fill in the form below.
Sofia Tyson is the Senior Content Manager at Juro, where she has spent years as a legal content strategist and writer, specializing in legal tech and contract management.
Sofia has a Bachelor of Laws (LLB) from the University of Leeds School of Law where she studied the intersection of law and technology in detail and received the Hughes Discretionary Award for outstanding performance. Following her degree, Sofia's legal research on GDPR consent requirements was published in established law journals and hosted on HeinOnline, and she has spent the last five years researching and writing about contract processes and technology.
Before joining Juro, Sofia gained hands-on experience through short work placements at leading international law firms, including Allen & Overy. She also completed the Sutton Trust’s Pathways to Law and Pathways to Law Plus programs over the course of five years, building a deep understanding of the legal landscape and completing pro-bono legal volunteering.
Sofia is passionate about making the legal profession more accessible, and she has appeared in several publications discussing alternative legal careers.
With a flood of tools on the market and hype outpacing understanding, it’s hard to know where to begin.
Should you build or buy? What will actually move the needle for your legal team? And what should you have in place before rolling anything out?
In this guide, we cut through the noise. Whether you're a seasoned GC or just starting your legal ops journey, you’ll find practical advice on evaluating Legal AI, making smart decisions, and avoiding costly missteps.
Introduction to legal AI
What started as cautious experiments with clause extraction or keyword tagging has evolved into a wave of transformative legal AI technologies, from generative AI drafting entire contracts to agentic tools adding redlines to legal documents autonomously.
What once sat on innovation roadmaps is now in production, powering workflows across in-house teams, law firms, and legal tech providers.
The early 2010s saw a flurry of interest in AI for law, mainly rule-based engines and machine learning tools that could parse documents or score risk. But adoption was slow. Tools were clunky. Legal work was too nuanced, too contextual — and frankly, most AI wasn’t good enough to trust.
Then came 2022. The release of large language models (LLMs) like GPT-3.5 and GPT-4 changed the equation. For the first time, lawyers could interact with AI in natural language — and the AI could respond with outputs that felt not just usable, but genuinely useful.
That leads us nicely into where we are today, with more than 90 per cent of in-house lawyers using these generative AI tools either daily or weekly, and almost all of the lawyers we recently surveyed saying they believe that AI will change their job over the course of a year.
Results from our recent survey of in-house lawyers.
Key benefits of AI in law
There’s been no shortage of noise about AI replacing lawyers. It’s a conversation that’s sparked anxiety in the legal profession for years, and understandably so.
But at Juro, we don’t believe it’ll go that way.
AI isn’t replacing lawyers — it’s making them faster, sharper, and more effective at what they do best. For in-house teams, that means fewer manual tasks, shorter contract cycles, and more time to focus on the strategic work that actually moves the needle for the business.
Legal AI is at its best when it’s helping lawyers get rid of the repetitive, time-consuming work — the boilerplate, the clause comparisons, the endless formatting — and letting them set the rules that AI then follows. It’s not about giving up control. It’s about applying your judgement where it counts, and letting legal automation do the rest.
Essentially, this benefits lawyers in three key ways.
1. More strategic work, less repetitive admin
By taking care of the high-volume, low-value tasks — like reviewing standard terms, checking for clause consistency, or approving routine contracts like NDAs — AI frees up legal teams to focus on the work that really needs their input: advising on risk, negotiating complex deals, and shaping business strategy.
By doing this, legal AI shifts legal’s role from reactive support to proactive partner: advising on business risk, navigating complex deals, and contributing to strategic decisions without being buried in admin.
2. Better decisions, powered by data
AI tools can analyse large volumes of contract data quickly — pulling trends, identifying anomalies, and surfacing hidden risks across your contract portfolio.
For example, you can instantly see how termination clauses vary across hundreds of MSAs, or which contracts are missing key obligations. That kind of insight used to take days, but now it takes seconds.
Legal AI tools like Juro leverage AI to extract key datapoints from your contracts, making it ready to report on.
3. Real cost savings
AI reduces the need for external legal spend on routine matters — like contract review, redlining, and first-pass due diligence because it automates it.
It also cuts internal time spent on manual legal work, helping smaller teams support growing businesses without scaling headcount at the same pace. The net result: a more efficient legal function that delivers more value, without the extra cost.
In an environment where AI is redefining value in the legal industry, AI-driven solutions and alternative providers are proving that high-quality legal work doesn’t have to come with excessive fees or inefficiencies” - Richard Mabey, CEO at Juro
Legal AI use cases
Legal AI has matured from a niche experiment into a core capability. Today, it’s not just helping lawyers work faster — it’s transforming how legal teams operate, how they collaborate with the business, and how they deliver value at scale.
Below are the key ways in-house legal teams are already putting AI to work — not just in contracts, but across the full legal function — along with the tools helping them do it.
1. Contract drafting and review at scale
Legal AI tools like Juro’s can draft entire contracts in seconds, drawing from automated contract templates, playbooks, and prior agreements to ensure alignment with company standards. But it doesn’t stop at drafting.
Juro’s agentic AI also reviews contracts for deviations from pre-approved positions, suggests redlines, and can even explain the rationale behind its edits — saving lawyers hours per document.
These capabilities are especially valuable in high-volume areas like NDAs, MSAs, or SLAs, where speed matters but risk can’t be compromised. Instead of manually combing through each clause, lawyers can trust AI to handle the heavy lifting, surfacing only the outliers that truly need attention.
Popular legal AI tools for this:
Juro – Drafts, reviews, and redlines contracts end-to-end using generative and agentic AI.
Harvey – LLM-powered legal assistant for sophisticated contract review and regulatory analysis.
Spellbook – Contract review assistant embedded into Microsoft Word.
Juro's contract review agent is now available in Slack.
Juro’s AI-powered contract review agent now allows third-party contracts to be reviewed and redlined against your contract playbooks. This is available in Juro, Slack, Microsoft Teams, and Microsoft Word, making this fully automated contract review functionality accessible where your teams already work.
If that sounds like something you'd benefit from, hit the button below to see it in action.
2. Playbook enforcement and clause standardisation
AI tools can monitor whether incoming contracts conform to your standard contract playbook — and flag deviations in real time. For example, if a supplier sends over terms with an unusually aggressive indemnity clause, the AI can detect it instantly, suggest fallback language, and escalate to legal if thresholds are exceeded.
This kind of automated consistency reduces the likelihood of risk slipping through unnoticed, and ensures contracts are aligned with evolving company policies.
Popular legal AI tools for this:
Juro – Flags clause deviations and automates redlines based on pre-approved playbooks.
Robin AI – Automatically applies fallback logic and clause swaps at scale.
Lexis+ AI – Matches language in incoming contracts to internal policy documents.
3. Legal intake and ticketing
Many in-house teams are also using AI to streamline legal intake. Instead of managing ad-hoc emails or Slack messages, AI tools can categorise, route, and even respond to incoming legal requests.
For example, if someone asks, “Can I use this contractor agreement for a freelancer in Germany?”, an AI tool might pull up the relevant template, check jurisdiction-specific requirements, and respond with a recommended next step — or escalate to legal for input if needed.
Popular legal AI tools for this:
Juro – Contract intake forms and AI-powered routing within contract workflows.
LawVu – Legal ticketing with automation and self-service intake logic.
TangoEye – NLP-driven request categorisation and response handling.
4. Litigation support and due diligence
In more advanced use cases, legal AI is also assisting with litigation preparation or M&A diligence. By reviewing large volumes of contracts and surfacing relevant clauses, obligations, and risks, AI dramatically reduces the time needed to prepare disclosures or identify red flags.
Some teams use AI to flag renewal terms, change-of-control clauses, or exclusivity provisions — insights that would previously take days to compile manually.
Popular legal AI tools for this:
RelativityOne – Industry standard for AI-driven eDiscovery and document review.
DISCO AI – Advanced predictive coding for litigation preparation. Kira Systems – Used in both litigation prep and compliance audits.
5. Policy, regulatory and compliance monitoring
AI tools are now able to analyse regulations and map them to internal policies — ensuring compliance and flagging gaps.
From GDPR audits to identifying where new employment laws affect existing templates, AI can highlight risks without a team spending weeks in spreadsheets.
Popular legal AI tools for this:
Harvey – Can be used to summarise and contextualise evolving regulations.
Imprima Smart Compliance – Monitors legal obligations across jurisdictions and ties them to internal docs.
Relativity AI – Applied in compliance investigations and monitoring large datasets for regulatory issues.
6. Knowledge management and precedent search
In-house teams often have hundreds of legacy contracts, memos, or policies. However, surfacing relevant ones quickly can be near impossible.
With AI-powered search and summarisation, teams can find the “closest match” precedent or playbook in seconds, transforming how legal knowledge is accessed and reused.
Popular legal AI tools for this:
Kira Systems – Extracts and classifies data from thousands of legacy contracts.
Lexion – Indexes contract repositories and enables fast clause- or metadata-level search.
Evisort – Surfacing precedent and clause variations across enterprise contract databases.
7. Strategic advice and business partnering
AI enables lawyers to focus on complex strategic work — advising on risk, supporting go-to-market teams, or unblocking deals — because it handles the repetitive low-value tasks.
But more than that, AI can help simulate legal outcomes, summarise risks, and even suggest negotiation strategies based on past deals.
Popular legal AI tools for this:
Juro – Automates contracting so legal can spend time advising on what matters.
CoCounsel (Casetext) – Used for rapid legal research, case summaries, and structured legal argument development.
Luminance – Applies legal reasoning to guide decision-making in M&A and audits.
8. Reporting, analytics, and insights
AI tools can extract contract metadata, identify clause trends, and benchmark across time or counterparties. That means teams can spot issues before they escalate — and make informed decisions faster.
For example, a GC can pull data for big questions like: “Which active contracts expose us to unlimited liability?” — and get an answer within seconds.
Popular legal AI tools for this:
Juro – Surfacing real-time contract insights and clause-level data across the business.
LinkSquares – Provides customisable contract dashboards and risk reports across contract portfolios.
Evisort – Advanced AI analytics and reporting features for enterprise teams.
Types of legal AI tools
The legal AI market is booming, but it’s also fragmented, fast-moving, and often hard to decode.
What’s the difference between an AI assistant and a contract lifecycle platform? Between copilots and agents? Between a “legal AI tool” and just good software that happens to use AI?
In reality, not all legal AI tools are created equal. And when you’re choosing a platform to bet on understanding what category a tool sits in — and what problem it’s best at solving — is just as important as what tech it uses under the hood.
Broadly speaking, legal AI tools can fit into a few categories:
Type of legal AI tool
Summary
Benefits
Drawbacks
Point solutions(e.g Spellbook, LawGeex, RobinAI, Harvey, PaxtonAI)
Standalone tools designed to solve a single legal task or use case — usually very well. Think redlining, legal research, due diligence, or clause comparison.
Fast to deploy and experiment with. Often best-in-class for the task at hand. Ideal for filling gaps in existing workflows. Usually lower cost and commitment than full platforms.
Risk of tool sprawl and disconnected data. May require legal ops to “glue” tools together. Can’t scale easily into adjacent workflows. Vary in explainability and configurability
AI embedded into CLMs(e.g Juro, Ironclad, Linksqaures, Evisort)
Contract lifecycle and legal operations platforms that embed AI throughout — from drafting and negotiation to approvals and analytics. These tools are built for end-to-end workflows, not just single tasks.
Centralises work into one platform. Reduces handoffs and manual steps. AI is context-aware (based on templates, history, users). Provides strong governance, access control, and audit trails.
Higher implementation effort upfront. May be overkill for smaller teams or narrow use cases. Some platforms bolt on AI later, rather than build natively. Limited flexibility if you want to heavily customise logic.
General purpose AI tools(e.g ChatGPT, Microsoft Copilot, Claude, Gemini)
Broad LLM-based tools or productivity platforms not built for legal, but used by lawyers via prompts or workflows. They’re flexible, accessible, and sometimes surprisingly powerful.
Very low barrier to entry and cost. Useful for experimentation, prototyping, or one-off tasks. Can be integrated into internal tools (e.g. Notion, Slack, Docs). Often good for summarisation, brainstorming, or clause drafting.
Outputs can be unreliable or lack legal nuance. Usually lack explainability or source-tracking. Risk of data exposure if not enterprise-grade. No built-in legal workflows or compliance logic.
Agents and Copilots(e.g Juro’s AI agent, Flank AI)
Copilots assist you by suggesting next steps. Agents go further — they complete tasks autonomously based on predefined logic and context.
Automate entire workflows, not just responses. Reduce manual involvement to near-zero. Can learn from prior interactions to personalise actions. Ideal for high-volume, high-consistency work.
Require more configuration and trust. Less suitable for tasks needing nuanced judgment. Still an emerging category — fewer mature examples.
Categories of legal AI software
There are also slightly more nuanced categories of legal AI software, with many focusing on delivering value at one particular stage of the contract lifecycle, for example.
1. Contract extraction software
Contract extraction software scans and parses contracts to pull out important information (like names, deadlines, or terms) and turns it into structured data. It's like turning messy PDFs into neat table views automatically, which makes it great for migrating old contracts or getting quick visibility into what’s in your agreements.
What it does
Extracts key metadata and clauses—like renewal dates, payment terms, governing law, and parties—from contracts into a structured format (e.g. spreadsheetsorcontract management platforms).
Reduces manual data entry, speeds up contract onboarding, and improves data accuracy.
Example use case
A legal ops team uses extraction software to import 1,000 legacy NDAs into a CLM system, tagging each with party names, dates, and key clauses for easy filtering.
Legal document generation software lets teams generate contracts by answering a few questions or syncing with a CRM—so instead of writing contracts manually, you're filling in blanks or letting software do it for you. It’s most helpful when creating lots of the same type of document.
What it does
Allows users to create custom contracts from templates by filling out questionnaires or integrating with CRM systems like HubSpot,SalesforceandPipedrive.
Where it's used
At the contract creation and negotiation stage, especially for high-volume agreements like NDAs orsales contracts.
Value delivered
Saves time, reduces errors, ensures consistency, and increases self-serve capabilitiesfor business teams.
Example use case
Sales reps use an automated NDA template connected to Salesforce to instantly generate compliant contracts without legal team involvement.
Contract review software behaves like a junior lawyer trained on your playbook—it scans contracts you're receiving (like from a vendor or partner), highlights risks, and even suggests changes. It speeds up review cycles and helps legal teams focus on the trickiest issues.
What it does
Analyzes incoming contracts and redlines them autonomously, identifying risky clauses, missing terms, and non-compliant language based on company playbooks. Also known ascontract redlining software.
Where it's used
During the contract negotiation phase, especially forthird-party paper.
Value delivered
Accelerates review cycles, supports faster deal closing, and ensures compliance with internal policies.
Example use case
A junior legal associate uses AI review software to quickly scan a vendor agreement, which flags an unfavorable indemnity clause for further review or proposes a new, more acceptable clause that aligns with your playbooks.
Rather than looking at contracts one at a time, contract intelligence software pulls insights from across a whole contract portfolio. It's like business analytics for your agreements, allowing you to spot the trends, risks, or revenue opportunities buried in thousands of pages of legal jargon.
What it does
Turnscontract datainto business intelligence by aggregating insights across large contract portfolios.
Where it's used
Post-signature, for risk monitoring, compliance tracking, and operational reporting.
Value delivered
Enables data-driven decisions by surfacing trends like renewal risks, common negotiation bottlenecks, orcontract value leakage.
Example use case
The finance team generates reports onauto-renewalsdue in the next quarter, pulled from thousands of supplier contracts.
Contract abstraction tools summarize contracts so you don’t have to read every word. They’re perfect for busy times like M&A, where you need to understand 500 agreements quickly. You get short-form versions showing the key obligations or clauses.
What it does
Summarizes key contractual terms and obligations into concise bullet points or summary documents. These are often referred to ascontract summaries, or abstracts.
Where it's used
Useful during M&A due diligence, audits, or handovers.
Value delivered
Speeds up understanding of large contract sets without needing full legal review of each.
Example use case
During an acquisition, a legal team uses abstraction tools to summarize 500 customer contracts, highlighting assignment clauses and termination rights.
An AI contracts generator is where generative AI meets legal drafting. You type a natural-language prompt like “create an NDA for a freelance designer,” and the software drafts it for you. It is one of the most basic AI solutions out there and is typically adopted by non-lawyers as a starting point.
What it does
Creates new contracts from scratch using natural language inputs, often driven by generative AI (e.g. "Generate an employment agreement for a London-based software engineer").
Contract analysis software doesn’t just extract data—it analyzes how your contracts stack up across a portfolio. It benchmarks terms, flags inconsistencies, and spots unusual patterns. Useful for audits, diligence, or just checking if you’re staying within policy.
What it does
Uses machine learning to understand, compare, and benchmark contracts or clauses across large datasets.
Where it's used
Both pre- and post-signature—for diligence, compliance audits, or contract portfolio analysis.
Value delivered
Reveals patterns and outliers in contractual terms that could pose risks or opportunities.
Example use case
A GC uses AI to analyze variation in liability caps across hundreds of supplier agreements, flagging those that diverge from standard terms and highlighting contractual risk.
This type of software scans and parses contracts to pull out important information (like names, deadlines, or terms) and turns it into structured data. It's like turning messy PDFs into organized Excel rows automatically—great for migrating old contracts or getting quick visibility into what’s in your agreements.
Legal AI chatbots vary in their scope. However, employees can typically ask them questions about contracts, clauses, or policies and receive instant, contextual answers based on your playbooks or past agreements. It helps legal scale support without drowning in repetitive questions.
What it does
Provides instant responses to legal queries via chat, often integrated into tools like Slack, Teams, or internal portals.
Where it's used
Throughout the contract lifecycle—helping users draft clauses, understand obligations, or request legal help.
Value delivered
Deflects routine legal questions, accelerates access to legal knowledge, and improves internal service levels.
Example use case
A chatbot trained on the company’s contract playbook helps sales reps understand fallback positions when a customer pushes back on a clause. contractual risk.
Unlike many tools that fit neatly into these boxes, Juro doesn’t just plug into one stage of the contract lifecycle — it combines functionality from multiple legal AI software categories into a single, browser-based platform, helping fast-scaling businesses manage contracts more efficiently from end to end.
Legal document automation
Juro empowers business teams to generate compliant contracts in seconds through structured templates and natural language AI prompts. Whether using smartfields, dropdowns, or AI-assisted clause generation, users create contracts without needing legal to get involved in every draft.
AI contract review
Juro’s AI Assistant reviews contracts in-browser, providing instant redlines, risk flags, and fallback suggestions aligned with your contract playbook. Best of all, this agentic AI means this all happens autonomously. This reduces legal review cycles and helps business teams close deals faster, even on third-party paper.
Contract abstraction and extraction
Juro’s AI identifies and pulls out key terms, clauses, and metadata from both legacy and in-flight contracts. This eliminates the need for manual tagging and makes contract data searchable, actionable, and audit-ready from day one, even for contracts on the other party’s paper.
Contract intelligence and analysis
With structured contract data captured at creation and enriched via AI, Juro delivers real-time insights into contract volumes, renewal dates, negotiation trends, and clause-level deviations—supporting smarter, data-led decisions across legal and commercial teams.
Emerging trends in legal AI
1. Agentic AI: the transition from assistive to autonomous
We’re in an era where AI doesn’t just help with tasks — it completes them.
Agentic AI refers to intelligent systems that can independently complete entire workflows, like reviewing a contract, redlining risky clauses, routing it to stakeholders, and even triggering approval flows.
Unlike simple prompt-based assistants, agentic tools make decisions based on context, user intent, and prior interactions — transforming them into digital teammates, not just tools.
Jake Jones, Co-founder of Flank, an AI agent, shared his great definition of agentic AI on our podcast recently:
Agents aren’t to be confused with copilots, though. There are some important distinctions between the two. To add some colour to this comparison, agents can perform tasks autonomously, whereas copilots can’t.
Let’s take a common AI use-case in legal: reviewing a contract. A copilot might read your contract and recommend stuff for you to do with it. Agree with this clause interpretation, action this reminder date, flag this deviation from your standard playbook, and so on.
An agent might read your contract, know all the context in the same way a copilot does - and instead of recommending actions, it takes them. It just does it.
That’s what we’re delivering to in-house legal and business teams. Juro’s AI contract review functionality enables teams to review and redline contracts with AI agents, meaning anyone can get approved redlines in a matter of minutes, without your GC on hand.
2. The build vs buy debate
Post-ChatGPT, it suddenly felt like every team could (and maybe should?) build their own legal AI tool. Open-source models were accessible, APIs were everywhere, and prompts became a new language.
It’s not quite the same as when YouTube made everyone a creator, but it’s close. So if software development has been democratized, what does it mean for one of the oldest tensions in legal technology: do you buy, or do you build?
If your pain point is summarizing a doc, you don’t need legal tech: you need ChatGPT. If you want to build a ticketing system, you don’t need legal tech: you need Zapier.
So far, so good. But … when you reach a certain level of complexity, or risk, or monetary value … your vibe-coded solutions will start to creak. And then break. Reviewing contracts in ChatGPT is a prime example.
Ultimately, you need to recognize where legal tech vendors can and should be adding value that can’t easily be reproduced with off-the-shelf tools.
You are not buying software. You are buying the experience and expertise of a vendor that deeply understands user behaviour and has iterated thousands of times to create the perfect solution. The question is, when you look at your vendors … do they deliver that?” - Richard Mabey, CEO at Juro
3. Explainable AI
In legal, trust isn’t a nice-to-have — it’s non-negotiable.
AI that delivers outputs without explanation — the so-called "black box" — might fly in consumer tools, but it simply doesn’t pass muster in a legal context. If your AI recommends deleting an indemnity clause, the very first question will be: why?
In practice, this means legal AI tools must now do more than provide answers. They need to:
Cite their sources: Point to the clause, playbook, or policy that supports their recommendation.
Show their logic: Explain how the decision was reached — was it pattern recognition? Risk weighting? Fallback logic?
Offer alternatives: Present options, not edicts — so humans stay in control of the final call.
Adapt to user feedback: Learn from overrides and rejections, building a feedback loop into the workflow.
Explainability transforms AI from an output engine into a partner in legal reasoning, and it’s the perfect aid for commercial teams that need to relay the explanation for redlines shared with counterparties, for example, without the support of an in-house lawyer.
Are you asking the right questions? Are you engaging in deep, thoughtful analysis? Can you communicate effectively with the business? These are the emerging markers of legal value.” - Richard Mabey, CEO at Juro
Legal AI agents
It’s early days, but the direction of travel is clear. Legal AI is moving from task-based tools to intelligent, proactive systems that work alongside lawyers — not just as assistants, but as autonomous teammates. In other words, AI agents.
These agents don’t just respond to prompts; they can follow goals, apply logic, and handle multi-step workflows independently, managing parts of the legal process from start to finish without constant human input.
For example, Juro’s AI agent can handle entire workflows: reviewing third-party contracts against your positions, applying redlines, notifying legal if something falls outside the rules, and escalating only when needed.
Results from our recent survey of in-house lawyers.
Legal’s role in regulating AI
As legal AI tools become more capable, it’s essential to stay grounded in the risks as well as the rewards.
For in-house legal teams, that means thinking carefully about how AI is deployed and where the boundaries should be.
Who’s responsible if the AI makes a mistake? How do you validate outputs? What data is being used to generate results? Should that data be inputted at all, and if so, how is it protected?
These aren’t hypothetical concerns. They are questions legal teams need to answer today.
But how can they do that when regulators themselves appear to be falling behind? When we surveyed in-house lawyers earlier this year, the vast majority believed that regulators had either little or no understanding of the technology they’re regulating. In fact, 66 per cent felt that way.
Results from our recent survey of in-house lawyers.
However, that doesn’t excuse lawyers from their duty to use AI responsibly and enforce that same expectation across their business. After all, we’re already seeing high-profile cases whereby companies leveraging AI in contentious ways have faced the letter of the law:
And that’s not all. New data has revealed that entry-level roles have dropped by a third since the launch of ChatGPT, meaning the advent of widely available AI tools has already had a real and measurable impact on the career prospects of the younger generation. This is true even in larger, well-resourced organizations like the UK’s Big Four, which are slashing graduate roles.
Add to that the fact that AI runs on energy, not magic, and it’s easy to visualize the impact AI has, and will continue to have, on environmental resources too:
Most large-scale AI deployments are housed in data centres, including those operated by cloud service providers. These data centres can take a heavy toll on the planet.” - The United Nations Environment Programme
Why are we labouring these points in a guide about legal AI? Well, it’s easy to think of legal AI as just another tooling decision: which solution should we buy to speed up contract reviews?
But the role of in-house legal goes far deeper than procurement. As AI reshapes how businesses build, operate and sell, legal sits at the centre of that change — advising on risks, enabling innovation, and setting the rules for responsible use.
Key considerations for lawyers
Ultimately, any in-house lawyer should have these three questions front of mind:
1. How can we responsibly build and sell AI?
If your business is developing AI products, legal has a key role in ensuring they're safe, compliant, and transparent from the outset. That means advising on IP rights, user consent, model explainability, and how risks are disclosed. It's not just about checking the box — it’s about helping build trust into the product.
2. How do we regulate AI use internally?
AI is being used across the business — by marketing, HR, sales, and ops. Legal teams should take the lead on setting internal guardrails: what’s allowed, what’s not, and where human oversight is required.That might mean drafting acceptable use policies, creating risk classifications for AI tools, or setting review thresholds for automated decisions.
Generative AI is transformational for legal work. But it’s not infallible. As a skilled lawyer or contract professional, your job is to engineer prompts and monitor output in a way that produces high quality output that you are prepared to stand behind.” - Michael Haynes, General Counsel at Juro
3. Are we implementing AI fairly and equitably?
We know by now that AI systems can inject bias into a decision-making process, just like humans can. In fact, studies have revealed that bias within AI often amplifies our own bias.
If those systems impact hiring, pricing, or customer interactions, the legal risks are very real, meaning in-house teams should proactively help ensure that fairness is baked into implementation. That includes reviewing training data, questioning outputs, and making sure AI isn’t reinforcing inequalities, even unintentionally.
Responsible AI means designing and using AI-powered systems in ways that align with human values and prevent harm.” - Michael Haynes, General Counsel at Juro
Legal AI implementation and adoption: best practices
Successful adoption isn’t just about picking a vendor or switching on a feature. It requires thoughtful planning, stakeholder buy-in, and iterative learning. We speak to lawyers daily, so we know firsthand that a lot of legal tech vendors are still failing their customers when it comes to adoption.
We know that CLM has an adoption problem. At Juro we take great pride in having the highest adoption rate in our category, according to independent G2 reviews. But there are solutions who I won’t name and shame - ones you’d have heard of - whose adoption rate is half of Juro’s” - Richard Mabey, CEO at Juro
Here’s how forward-thinking legal teams are making AI stick:
1. Start with a clearly defined problem, not the tech
AI isn’t a magic wand and it shouldn’t be adopted for its own sake. We know that contradicts what most organizations are hearing today, but the most effective implementations begin with a focused use case:
“We’re spending too long reviewing third-party contracts, and it’s stopping us from focusing on MSAs.”
“Sales can’t self-serve on NDAs without legal input, and it’s leading to longer sales cycles.”
“We have zero visibility into renewal management across our supplier agreements, and it’s leading to unexpected costs following missed auto-renewals.”
Like any project, begin by defining a pain point with measurable outcomes in mind, then explore how AI can address it. This is crucial because it enables you to identify which solutions actually solve your problems best, rather than which ones have the most hype.
2. Invest in its success (but rethink what “investment” looks like)
You don’t need to spend six months building clunky workflows in a legacy CLM to succeed with legal AI. But you do need to invest time and attention into the right things:
Refining the prompts and inputs that deliver the best results
Curating a contract playbook with clear guidance, fallback clauses, and risk thresholds
Teaching the tool how your business thinks about terms like liability, renewal, or exclusivity
Building simple, structured workflows that legal and business users can actually follow
After all, your output will only be as strong as your input, regardless of which tool you use. There’s little use investing in an expensive AI redlining solution only for it to mark your contracts up against guardrails that don’t match your own. Take the time to customize your workflows. You’ll thank yourself later.
Without metrics, momentum fizzles. We strongly recommend determining what success with your legal AI platform looks like early, and reporting on it consistently.
Key adoption and impact metrics might include:
Contracts created/reviewed with AI
Time saved per review
Reduction in legal escalations
Increased self-serve contract volume
Accuracy of AI vs human review
Time-to-sign improvements
Use these results to tell a compelling story internally — one that secures future or continued investment in legal AI tools that you know work.
The future isn’t about replacing lawyers; it’s about amplifying their capabilities. Legal AI tools will increasingly act as collaborative partners—working alongside teams to handle routine tasks, surface insights, and free up time for strategic work. This matters in scaling businesses, where lean legal teams are under pressure to deliver more with less.
For instance, AI-native contract management platforms already enable self-serve contracts. But what if these systems also had AI agents that could flag deviations from playbooks, highlight unusual contract terms, or benchmark agreements against market standards in real-time? This level of insight could redefine how legal teams approach their work.
Innovators who find ways to get at least some of that work at 1% of the price will win big. In their daily work, yes, (who wants to do grunt work?) but also in their career advancement" - Richard Mabey, CEO, Juro
A new era for legal teams
The future of legal AI tools is exciting and filled with potential. AI agents will bring unprecedented levels of efficiency, insight, and collaboration to legal teams.
But it’s not just about the technology itself; it’s about how these tools will reshape the role of legal in scaling businesses—enabling teams to work smarter, faster, and with more impact.
This is something we're working closely with legal teams on in 2025 and beyond. To be one of them, fill in the form below.
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Your privacy at a glance
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